Estimation of road traffic state at a multi-lanes controlled junction

We present in this paper a method for the estimation of traffic state at road junctions controlled with traffic lights. We assume mixed traffic where a proportion of vehicles are equipped with communication resources. The estimation of road traffic state uses information given by communicating vehicles. The method we propose is built upon a previously published method which was applied to estimate the traffic in the case where roads are composed of two lanes. In this paper, we consider the case where roads are composed of three lanes and we show that this solution can address the general case, where roads are composed of any number of lanes. We assume the geometry of the road junction is known, as well as its connections between incoming and outgoing lanes and roads. Using the location data provided by the communicating vehicles, first, we estimate some primary parameters including the penetration ratio of the probe vehicles, as well as the arrival rates of vehicles (equipped and non-equipped) per lane by introducing the assignment onto the lanes. Second, we give estimations of the queue length of the 3lanes road, without and with the additional information provided by the location of the communicating vehicles in the queue. We illustrate and discuss the proposed model with numerical simulations.

[1]  Donald R. McNeil,et al.  A solution to the fixed-cycle traffic light problem for compound Poisson arrivals , 1968 .

[2]  Jakob Erdmann,et al.  SUMO’s Lane-Changing Model , 2015 .

[3]  Peng Hao,et al.  Cycle-by-cycle intersection queue length distribution estimation using sample travel times , 2014 .

[4]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[5]  Nadir Farhi,et al.  Estimation of urban traffic state with probe vehicles , 2018, ArXiv.

[6]  R Akcelik,et al.  Time dependent expressions for delay, stop rate and queue length at traffic signals , 1980 .

[7]  A. Varga,et al.  THE OMNET++ DISCRETE EVENT SIMULATION SYSTEM , 2003 .

[8]  Henry X. Liu,et al.  Real-time queue length estimation for congested signalized intersections , 2009 .

[9]  Guillaume Leduc,et al.  Road Traffic Data: Collection Methods and Applications , 2008 .

[10]  Qiang Ji,et al.  Vehicle index estimation for signalized intersections using sample travel times , 2013 .

[11]  Pravin Varaiya,et al.  Max pressure control of a network of signalized intersections , 2013 .

[12]  Nikolas Geroliminis,et al.  Queue Profile Estimation in Congested Urban Networks with Probe Data , 2015, Comput. Aided Civ. Infrastructure Eng..

[13]  Mecit Cetin,et al.  Queue length estimation from probe vehicle location and the impacts of sample size , 2009, Eur. J. Oper. Res..

[14]  Gurcan Comert,et al.  Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters , 2016, Eur. J. Oper. Res..

[15]  Francesco Viti,et al.  Probabilistic models for queues at fixed control signals , 2010 .

[16]  Reinhard German,et al.  Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis , 2011, IEEE Transactions on Mobile Computing.

[17]  T. Koopmans,et al.  Studies in the Economics of Transportation. , 1956 .

[18]  F. Webster TRAFFIC SIGNAL SETTINGS , 1958 .

[19]  Jianfeng Zheng,et al.  Estimating traffic volumes for signalized intersections using connected vehicle data , 2017 .

[20]  Gurcan Comert,et al.  Simple analytical models for estimating the queue lengths from probe vehicles at traffic signals , 2013 .

[21]  A J Miller,et al.  The capacity of signalized intersections in Australia , 1968 .

[22]  Li Li,et al.  Urban traffic signal control with connected and automated vehicles: A survey , 2019, Transportation Research Part C: Emerging Technologies.

[23]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[24]  Xuegang Jeff Ban,et al.  Real time queue length estimation for signalized intersections using travel times from mobile sensors , 2011 .